Python在3D散点图中用线连接相邻点 [英] Python joining adjacent points with line in 3D scatter plot

问题描述

我有两组（x，y.z）坐标，这些坐标是我用Matplotlib在3D散点图中绘制的。现在，我想将每个生成的四边形与一个平面相连。
我已经看到了如何从

我的代码：

 从mpl_toolkits.mplot3d import numpy as np 
 import Axes3D 
 import matplotlib.pyplot as plt 
 
 def plot_figure（data）：
 fig = plt.figure（）
 ax = fig.gca（projection ='3d'）
 
 ax.scatter（data [： ，0]，data [:, 1]，data [:, 2]，
c ='r'，s = 20，线宽=无）
 
 ax.axis（'equal' ）
 ax.axis（'tight'）
 
 plt.show（）
 
 
 data = [[1900.，800.，442.82] ，[1900.，900.，463.04]，[1900.，1000。，
 473.06]，[1900.，1100。，485.07]，
 [1900.，1200。，498.63]，[ 1900.，1300。，513.83]，[1900.，1400。，536.1]，[
 1900.，1500。，551.29]，
 [1900.，1600。，566.5]，[1900。 ，1700。，581.65]，[1900.，1800。，603.91]，[
 2000.，800.，453.5]，
 [2000.，900.，473.75]，[2000.，1000。，487.14]，[2000.，1100。，499.48]，[
 2000。 ，1200。，513.39]，
 [2000.，1300。，528.92]，[2000.，1400。，551.85]，[2000.，1500。，567.35]，[
 2000.，1600 。，582.9]，
 [2000.，1700。，598.4]，[2000.，1800。，621.32]，[2100.，800.，464.23]，[
 2100.，900.， 485.34]，
 [2100.，1000.，502.87]，[2100.，1100.，515.71]，[2100.，1200.，530.13]，[
 2100.，1300.，546.14] ，
 [2100.，1400。，570.05]，[2100.，1500。，586.1]，[2100.，1600。，602.15]，[
 2100.，1700。，618.15]，
 [2100.，1800。，642.09]，[2200.，800.，474.94]，[2200.，900.，498.72]，[
 2200.，1000。，516.91]，
 [2200.，1100。，530.09]，[2200.，1200。，544.83]，[2200.1300。，561.2]，[
 2200.，1400。，585.8]，
 [ 2200.，1500。，602.17]，[2200.，1600。，618.55]，[2200.，1700。，634.89]，[
 2200.，1800。，659.46]，
 [2300。 ，800.，487.69]，[2300.，900.，513.43]，[2300.，1000。，532.64]，[
 2300.，1100。，546.32]，
 [ 2300.，1200。，561.57]，[2300.，1300。，578.43]，[2300.1400。，604.03]，[
 2300.，1500。，620.89]，
 [2300。 ，1600。，637.76]，[2300.，1700。，654.62]，[2300.，1800。，680.23]，[
 2400.，800.，500.75]，
 [2400.，900 。，526.83]，[2400.，1000。，546.69]，[2400.，1100。，560.71]，[
 2400.，1200。，576.3]，
 [2400.，1300。， 593.52]，[2400.，1400.，619.78]，[2400.，1500.，636.98]，[
 2400.，1600。，654.2]，
 [2400.，1700。，671.38] ，[2400.，1800。，697.66]，[2500.，800.，516.1]，[
 2500.，900.，542.71]，
 [2500.，1000。，563.6]，[ 2500.，1100。，578.12]，[2500.，1200。，594.18]，[
 2500.，1300。，611.89]，
 [2500.，1400。，639.17]，[2500。 ，1500。，656.87]，[2500.，1600。，674.58]，[
 2500.，1700。，692.26]，
 [2500.，1800。，719.53]，[2600.，800 。，530.05]，[2600.，900.，556.98]，[
 2600.，1000。，578.51]，
 [2600.，1100。，593.37]，[2600.，1200。， 609.77]，[2600.，1300.，627.81]，[
 2600.，1400。，655.76]，
 [2600.，1500。，673.82]，[2600.，1 600.，691.86]，[2600.，1700。，709.87]，[
 2600.，1800。，737.83]] 
 data = np.asarray（data）
 
 plot_figure（数据）
  

解决方案

您可以使用而不是单个多边形 plot_surface 或 plot_wireframe 图。为了能够使用它，您需要重塑数据以构成网格。在这种情况下，这很容易，因为数据已经按有用的顺序排列。

 将numpy导入为np 
从mpl_toolkits.mplot3d导入Axes3D 
导入matplotlib.pyplot as plt 
 
 #data =来自问题的数据
 data = np.asarray（data）
 
a，b = len（np.unique（data [：，0]）），len（np.unique（data [：，1]））
 
 X = data [：，0] .reshape（a，b）.T 
 Y =数据[：，1] .reshape（a，b）.T 
 Z =数据[：，2] .reshape（a，b）。 T 
 
图= plt.figure（）
斧=图gca（投影='3d'）
 
斧.plot_surface（X，Y，Z） 
 
 ax.axis（'等于'）
 ax.axis（'紧'）
 
 plt.show（）
  

或带有

  ax.plot_wireframe（X，Y，Z）
  

I have got two sets of (x,y.z) coordinates which I plotted in a 3D scatter plot with Matplotlib. Now, I would like to connect each resulting quadrilateral with a plane surface. I've seen how to draw a 2D polygon in a 3D space from Plotting 3D Polygons in python-matplotlib.

What I don't know how to do is to group the points of my set into 4 points groups in order to draw polygons. I would be happy even if I managed to connect each point with its neighbours with a straight line.

My set of points lies very close to a planar distribution; but it is just a set of coordinates, there is no constraint underneath it.

My code:

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

def plot_figure(data):
    fig = plt.figure()
    ax = fig.gca(projection='3d')

    ax.scatter(data[:, 0], data[:, 1], data[:, 2],
           c='r', s=20, linewidths=None)

    ax.axis('equal')
    ax.axis('tight')

    plt.show()


data = [[ 1900., 800., 442.82], [ 1900., 900., 463.04], [ 1900.,1000., 
473.06], [ 1900.,1100., 485.07], 
[ 1900.,1200., 498.63], [ 1900.,1300., 513.83], [ 1900.,1400., 536.1 ], [ 
1900.,1500., 551.29],
[ 1900.,1600., 566.5 ], [ 1900.,1700., 581.65], [ 1900.,1800., 603.91], [ 
2000., 800., 453.5 ], 
[ 2000., 900., 473.75], [ 2000.,1000., 487.14], [ 2000.,1100., 499.48], [ 
2000.,1200., 513.39],
[ 2000.,1300., 528.92], [ 2000.,1400., 551.85], [ 2000.,1500., 567.35], [ 
2000.,1600., 582.9 ], 
[ 2000.,1700., 598.4 ], [ 2000.,1800., 621.32], [ 2100., 800., 464.23], [ 
2100., 900., 485.34],
[ 2100.,1000., 502.87], [ 2100.,1100., 515.71], [ 2100.,1200., 530.13], [ 
2100.,1300., 546.14], 
[ 2100.,1400., 570.05], [ 2100.,1500., 586.1 ], [ 2100.,1600., 602.15], [ 
2100.,1700., 618.15], 
[ 2100.,1800., 642.09], [ 2200., 800., 474.94], [ 2200., 900., 498.72], [ 
2200.,1000., 516.91], 
[ 2200.,1100., 530.09], [ 2200.,1200., 544.83], [ 2200.,1300., 561.2 ], [ 
2200.,1400., 585.8 ], 
[ 2200.,1500., 602.17], [ 2200.,1600., 618.55], [ 2200.,1700., 634.89], [ 
2200.,1800., 659.46], 
[ 2300., 800., 487.69], [ 2300., 900., 513.43], [ 2300.,1000., 532.64], [ 
2300.,1100., 546.32], 
[ 2300.,1200., 561.57], [ 2300.,1300., 578.43], [ 2300.,1400., 604.03], [ 
2300.,1500., 620.89], 
[ 2300.,1600., 637.76], [ 2300.,1700., 654.62], [ 2300.,1800., 680.23], [ 
2400., 800., 500.75], 
[ 2400., 900., 526.83], [ 2400.,1000., 546.69], [ 2400.,1100., 560.71], [ 
2400.,1200., 576.3 ], 
[ 2400.,1300., 593.52], [ 2400.,1400., 619.78], [ 2400.,1500., 636.98], [ 
2400.,1600., 654.2 ], 
[ 2400.,1700., 671.38], [ 2400.,1800., 697.66], [ 2500., 800., 516.1 ], [ 
2500., 900., 542.71], 
[ 2500.,1000., 563.6 ], [ 2500.,1100., 578.12], [ 2500.,1200., 594.18], [ 
2500.,1300., 611.89],
[ 2500.,1400., 639.17], [ 2500.,1500., 656.87], [ 2500.,1600., 674.58], [ 
2500.,1700., 692.26],
[ 2500.,1800., 719.53], [ 2600., 800., 530.05], [ 2600., 900., 556.98], [ 
2600.,1000., 578.51],
[ 2600.,1100., 593.37], [ 2600.,1200., 609.77], [ 2600.,1300., 627.81], [ 
2600.,1400., 655.76],
[ 2600.,1500., 673.82], [ 2600.,1600., 691.86], [ 2600.,1700., 709.87], [ 
2600.,1800., 737.83]]
data = np.asarray(data)

plot_figure (data)

解决方案

Instead of individual polygons you may use a plot_surface or plot_wireframe plot. To be able to use this, you need to reshape your data to constitute a grid. In this case, this is rather easy, as the data is already in a useful order.

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

#data = data from the question
data = np.asarray(data)

a,b = len(np.unique(data[:,0])),  len(np.unique(data[:,1]))

X = data[:,0].reshape(a,b).T
Y = data[:,1].reshape(a,b).T
Z = data[:,2].reshape(a,b).T

fig = plt.figure()
ax = fig.gca(projection='3d')

ax.plot_surface(X,Y,Z)

ax.axis('equal')
ax.axis('tight')

plt.show()

or with

ax.plot_wireframe(X,Y,Z)

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